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      Detection of compound structures by region group selection from hierarchical segmentations

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      Author
      Akçay, H. Gökhan
      Aksoy, Selim
      Date
      2016-07
      Source Title
      International Geoscience and Remote Sensing Symposium, (IGARSS) 2016
      Publisher
      IEEE
      Pages
      5095 - 5098
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      Detection of compound structures that are comprised of different arrangements of simpler primitive objects has been a challenging problem as commonly used bag-of-words models are limited in capturing spatial information. We have developed a generic method that considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and detects new instances of compound structures through automatic selection of subsets of candidate regions from a hierarchical segmentation by maximizing the likelihood of their individual appearances and relative spatial arrangements. In this paper, we extend the model to handle different types of primitive objects that come from multiple hierarchical segmentations. Results are shown for the detection of different types of housing estates in a WorldView-2 image. © 2016 IEEE.
      Keywords
      Contextual modeling
      Markov random field
      Object detection
      Spatial relationships
      Compounds
      Buildings
      Image edge detection
      Markov processes
      Image segmentation
      Random variables
      Context modeling
      Permalink
      http://hdl.handle.net/11693/37732
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/IGARSS.2016.7730328
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      • Department of Computer Engineering 1409
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